David Martens - RankMe

115 papers A* 1Journal 95Unranked 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	An Agentic Approach to Generating XAI-Narratives. CoRR Yifan He, David Martens
2026	J	`jnl`	Calculating and Visualizing Feature Importance Values for Counterfactual Explanations. IEEE Access Bjorge Meulemeester, Raphael Mazzine Barbosa de Oliveira, Yifan He, David Martens
2026	J	`jnl`	On the Definition and Detection of Cherry-Picking in Counterfactual Explanations. CoRR James Hinns, Sofie Goethals, Stephan Van der Veeken, Theodoros Evgeniou, David Martens
2026	J	`jnl`	Would a Large Language Model Pay Extra for a View? Inferring Willingness to Pay from Subjective Choices. CoRR Manon Reusens, Sofie Goethals, Toon Calders, David Martens
2025	J	`jnl`	Aggregating Local Saliency Maps for Semi-Global Explainable Image Classification. CoRR James Hinns, David Martens
2025	J	`jnl`	Beware of "Explanations" of AI. CoRR David Martens, Galit Shmueli, Theodoros Evgeniou, Kevin Bauer, Christian Janiesch, Stefan Feuerriegel, Sebastian Gabel, Sofie Goethals, Travis Greene, Nadja Klein, Mathias Kraus, Niklas Kühl, Claudia Perlich, Wouter Verbeke, Alona Zharova, Patrick Zschech, Foster J. Provost
2025	J	`jnl`	Cash or Comfort? How LLMs Value Your Inconvenience. CoRR Mateusz Cedro, Timour Ichmoukhamedov, Sofie Goethals, Yifan He, James Hinns, David Martens
2025	J	`jnl`	Exploring the generalization of LLM truth directions on conversational formats. CoRR Timour Ichmoukhamedov, David Martens
2025	—	`conf`	Exposing Shortcuts in Image Classification by Aggregating Counterfactuals. IJCCI (3) James Hinns, David Martens
2025	J	`jnl`	From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI. CoRR Galit Shmueli, David Martens, Jaewon Yoo, Travis Greene
2025	—	`conf`	GraphXAIN: Narratives to Explain Graph Neural Networks. xAI (3) Mateusz Cedro, David Martens
2025	J	`jnl`	Monetization could corrupt algorithmic explanations. AI Soc. Travis Greene, Sofie Goethals, David Martens, Galit Shmueli
2025	J	`jnl`	Tell me a story! Narrative-driven XAI with Large Language Models. Decis. Support Syst. David Martens, James Hinns, Camille Dams, Mark Vergouwen, Theodoros Evgeniou
2024	J	`jnl`	A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data. Eur. J. Oper. Res. Raphael Mazzine Barbosa de Oliveira, Kenneth Sörensen, David Martens
2024	J	`jnl`	Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics. CoRR Sofie Goethals, Toon Calders, David Martens
2024	J	`jnl`	Can metafeatures help improve explanations of prediction models when using behavioral and textual data? Mach. Learn. Yanou Ramon, David Martens, Theodoros Evgeniou, Stiene Praet
2024	J	`jnl`	Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. Eur. J. Oper. Res. Koen W. De Bock, Kristof Coussement, Arno De Caigny, Roman Slowinski, Bart Baesens, Robert N. Boute, Tsan-Ming Choi, Dursun Delen, Mathias Kraus, Stefan Lessmann, Sebastián Maldonado, David Martens, María Óskarsdóttir, Carla Vairetti, Wouter Verbeke, Richard Weber
2024	J	`jnl`	Exposing Image Classifier Shortcuts with Counterfactual Frequency (CoF) Tables. CoRR James Hinns, David Martens
2024	J	`jnl`	GraphXAIN: Narratives to Explain Graph Neural Networks. CoRR Mateusz Cedro, David Martens
2024	J	`jnl`	How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives. CoRR Timour Ichmoukhamedov, James Hinns, David Martens
2024	J	`jnl`	NICE: an algorithm for nearest instance counterfactual explanations. Data Min. Knowl. Discov. Dieter Brughmans, Pieter Leyman, David Martens
2024	J	`jnl`	PreCoF: counterfactual explanations for fairness. Mach. Learn. Sofie Goethals, David Martens, Toon Calders
2023	J	`jnl`	Calculating and Visualizing Counterfactual Feature Importance Values. CoRR Bjorge Meulemeester, Raphael Mazzine Barbosa de Oliveira, David Martens
2023	J	`jnl`	Disagreement amongst counterfactual explanations: How transparency can be deceptive. CoRR Dieter Brughmans, Lissa Melis, David Martens
2023	J	`jnl`	Erratum to. Online Soc. Networks Media Stiene Praet, David Martens, Peter Van Aelst
2023	—	`conf`	Explainability Methods to Detect and Measure Discrimination in Machine Learning Models. EWAF Sofie Goethals, David Martens, Toon Calders
2023	J	`jnl`	How sustainable is "common" data science in terms of power consumption? Sustain. Comput. Informatics Syst. Bjorge Meulemeester, David Martens
2023	—	`conf`	Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem. PKDD/ECML Workshops (3) Sofie Goethals, David Martens, Theodoros Evgeniou
2023	J	`jnl`	Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem. CoRR Sofie Goethals, David Martens, Theodoros Evgeniou
2023	J	`jnl`	Monetizing Explainable AI: A Double-edged Sword. CoRR Travis Greene, Sofie Goethals, David Martens, Galit Shmueli
2023	J	`jnl`	Tell Me a Story! Narrative-Driven XAI with Large Language Models. CoRR David Martens, Camille Dams, James Hinns, Mark Vergouwen
2023	J	`jnl`	The Impact of Cloaking Digital Footprints on User Privacy and Personalization. CoRR Sofie Goethals, Sandra C. Matz, Foster J. Provost, Yanou Ramon, David Martens
2023	J	`jnl`	The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks. ACM Trans. Intell. Syst. Technol. Sofie Goethals, Kenneth Sörensen, David Martens
2023	J	`jnl`	Unveiling the Potential of Counterfactuals Explanations in Employability. CoRR Raphael Mazzine Barbosa de Oliveira, Sofie Goethals, Dieter Brughmans, David Martens
2022	J	`jnl`	Barriers to academic data science research in the new realm of algorithmic behaviour modification by digital platforms. Nat. Mach. Intell. Travis Greene, David Martens, Galit Shmueli
2022	J	`jnl`	Explainable image classification with evidence counterfactual. Pattern Anal. Appl. Tom Vermeire, Dieter Brughmans, Sofie Goethals, Raphael Mazzine Barbossa de Oliveira, David Martens
2022	J	`jnl`	How sustainable is "common" data science in terms of power consumption? CoRR Bjorge Meulemeester, David Martens
2022	J	`jnl`	The non-linear nature of the cost of comprehensibility. J. Big Data Sofie Goethals, David Martens, Theodoros Evgeniou
2022	J	`jnl`	The privacy issue of counterfactual explanations: explanation linkage attacks. CoRR Sofie Goethals, Kenneth Sörensen, David Martens
2021	J	`jnl`	A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data. CoRR Raphael Mazzine, David Martens
2021	J	`jnl`	Explainable AI for Psychological Profiling from Behavioral Data: An Application to Big Five Personality Predictions from Financial Transaction Records. Inf. Yanou Ramon, R. A. Farrokhnia, Sandra C. Matz, David Martens
2021	J	`jnl`	Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data. CoRR Yanou Ramon, Sandra C. Matz, R. A. Farrokhnia, David Martens
2021	—	`conf`	How to Choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice. PKDD/ECML Workshops (1) Tom Vermeire, Thibault Laugel, Xavier Renard, David Martens, Marcin Detyniecki
2021	J	`jnl`	How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice. CoRR Tom Vermeire, Thibault Laugel, Xavier Renard, David Martens, Marcin Detyniecki
2021	J	`jnl`	NICE: An Algorithm for Nearest Instance Counterfactual Explanations. CoRR Dieter Brughmans, David Martens
2021	J	`jnl`	Node classification over bipartite graphs through projection. Mach. Learn. Marija Stankova, Stiene Praet, David Martens, Foster J. Provost
2021	J	`jnl`	Patterns of democracy? Social network analysis of parliamentary Twitter networks in 12 countries. Online Soc. Networks Media Stiene Praet, David Martens, Peter Van Aelst
2021	J	`jnl`	Predictive modeling to study lifestyle politics with Facebook likes. EPJ Data Sci. Stiene Praet, Peter Van Aelst, Patrick van Erkel, Stephan Van der Veeken, David Martens
2021	J	`jnl`	Understanding Consumer Preferences for Explanations Generated by XAI Algorithms. CoRR Yanou Ramon, Tom Vermeire, Olivier Toubia, David Martens, Theodoros Evgeniou
2020	J	`jnl`	A benchmarking study of classification techniques for behavioral data. Int. J. Data Sci. Anal. Sofie De Cnudde, David Martens, Theodoros Evgeniou, Foster J. Provost
2020	J	`jnl`	A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C. Adv. Data Anal. Classif. Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou
2020	J	`jnl`	Customs fraud detection. Pattern Anal. Appl. Jellis Vanhoeyveld, David Martens, Bruno Peeters
2020	J	`jnl`	Efficient Parcel Delivery by Predicting Customers' Locations. Decis. Sci. Stiene Praet, David Martens
2020	J	`jnl`	Explainable Image Classification with Evidence Counterfactual. CoRR Tom Vermeire, David Martens
2020	J	`jnl`	Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data. CoRR Yanou Ramon, David Martens, Theodoros Evgeniou, Stiene Praet
2020	J	`jnl`	Value-added tax fraud detection with scalable anomaly detection techniques. Appl. Soft Comput. Jellis Vanhoeyveld, David Martens, Bruno Peeters
2019	J	`jnl`	Counterfactual Explanation Algorithms for Behavioral and Textual Data. CoRR Yanou Ramon, David Martens, Foster J. Provost, Theodoros Evgeniou
2019	J	`jnl`	Dance Hit Song Prediction. CoRR Dorien Herremans, David Martens, Kenneth Sörensen
2019	J	`jnl`	Deep Learning on Big, Sparse, Behavioral Data. Big Data Sofie De Cnudde, Yanou Ramon, David Martens, Foster J. Provost
2019	—	`conf`	Wearable Multimodal Stethoscope Patch for Wireless Biosignal Acquisition and Long-Term Auscultation. EMBC Michael Klum, Fabian Leib, Casper Oberschelp, David Martens, Alexandru-Gabriel Pielmus, Timo Tigges, Thomas Penzel, Reinhold Orglmeister
2019	J	`jnl`	What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance. J. Oper. Res. Soc. Sofie De Cnudde, Julie Moeyersoms, Marija Stankova, Ellen Tobback, Vinayak Javaly, David Martens
2018	J	`jnl`	Imbalanced classification in sparse and large behaviour datasets. Data Min. Knowl. Discov. Jellis Vanhoeyveld, David Martens
2018	J	`jnl`	Wallenius Bayes. Mach. Learn. Enric Junqué de Fortuny, David Martens, Foster J. Provost
2017	J	`jnl`	Bankruptcy prediction for SMEs using relational data. Decis. Support Syst. Ellen Tobback, Tony Bellotti, Julie Moeyersoms, Marija Stankova, David Martens
2017	J	`jnl`	RULEM: A novel heuristic rule learning approach for ordinal classification with monotonicity constraints. Appl. Soft Comput. Wouter Verbeke, David Martens, Bart Baesens
2016	—	`ch.`	Composer Classification Models for Music-Theory Building. Computational Music Analysis Dorien Herremans, David Martens, Kenneth Sörensen
2016	J	`jnl`	Explaining Classification Models Built on High-Dimensional Sparse Data. CoRR Julie Moeyersoms, Brian Dalessandro, Foster J. Provost, David Martens
2016	J	`jnl`	Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics. MIS Q. David Martens, Foster J. Provost, Jessica Clark, Enric Junqué de Fortuny
2015	J	`jnl`	Active Learning-Based Pedagogical Rule Extraction. IEEE Trans. Neural Networks Learn. Syst. Enric Junqué de Fortuny, David Martens
2015	J	`jnl`	Classification and Generation of Composer-Specific Music Using Global Feature Models and Variable Neighborhood Search. Comput. Music. J. Dorien Herremans, Kenneth Sörensen, David Martens
2015	J	`jnl`	Comprehensible software fault and effort prediction: A data mining approach. J. Syst. Softw. Julie Moeyersoms, Enric Junqué de Fortuny, Karel Dejaeger, Bart Baesens, David Martens
2015	J	`jnl`	Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design. Inf. Syst. Res. Foster J. Provost, David Martens, Alan Murray
2015	J	`jnl`	Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector. Decis. Support Syst. Julie Moeyersoms, David Martens
2015	—	`conf`	Iteratively refining SVMs using priors. IEEE BigData Enric Junqué de Fortuny, Theodoros Evgeniou, David Martens, Foster J. Provost
2015	J	`jnl`	Loyal to your city? A data mining analysis of a public service loyalty program. Decis. Support Syst. Sofie De Cnudde, David Martens
2015	J	`jnl`	To tune or not to tune: rule evaluation for metaheuristic-based sequential covering algorithms. Data Min. Knowl. Discov. Bart Minnaert, David Martens, Manu De Backer, Bart Baesens
2014	J	`jnl`	A Comment on "Correlation as a Heuristic for Accurate and Comprehensible Ant Colony Optimization-Based Classifiers". IEEE Trans. Evol. Comput. Bart Minnaert, David Martens
2014	A*	`conf`	Corporate residence fraud detection. KDD Enric Junqué de Fortuny, Marija Stankova, Julie Moeyersoms, Bart Minnaert, Foster J. Provost, David Martens
2014	J	`jnl`	Evaluating and understanding text-based stock price prediction models. Inf. Process. Manag. Enric Junqué de Fortuny, Tom De Smedt, David Martens, Walter Daelemans
2014	J	`jnl`	Explaining Data-Driven Document Classifications. MIS Q. David Martens, Foster J. Provost
2014	J	`jnl`	Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state. J. Oper. Res. Soc. Ellen Tobback, David Martens, Tony Van Gestel, Bart Baesens
2014	J	`jnl`	Social network analysis for customer churn prediction. Appl. Soft Comput. Wouter Verbeke, David Martens, Bart Baesens
2013	J	`jnl`	Predictive Modeling With Big Data: Big Data Enric Junqué de Fortuny, David Martens, Foster J. Provost
2012	—	`conf`	Active Learning Based Rule Extraction for Regression. ICDM Workshops Enric Junqué de Fortuny, David Martens
2012	J	`jnl`	Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Trans. Software Eng. Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens
2012	J	`jnl`	Media coverage in times of political crisis: A text mining approach. Expert Syst. Appl. Enric Junqué de Fortuny, Tom De Smedt, David Martens, Walter Daelemans
2012	J	`jnl`	New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. Eur. J. Oper. Res. Wouter Verbeke, Karel Dejaeger, David Martens, Joon Hur, Bart Baesens
2012	—	`conf`	Towards a Particle Swarm Optimization-Based Regression Rule Miner. ICDM Workshops Bart Minnaert, David Martens
2011	J	`jnl`	Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Syst. Appl. Wouter Verbeke, David Martens, Christophe Mues, Bart Baesens
2011	J	`jnl`	Editorial survey: swarm intelligence for data mining. Mach. Learn. David Martens, Bart Baesens, Tom Fawcett
2011	J	`jnl`	Guest Editorial White Box Nonlinear Prediction Models. IEEE Trans. Neural Networks Bart Baesens, David Martens, Rudy Setiono, Jacek M. Zurada
2011	J	`jnl`	Identifying financially successful start-up profiles with data mining. Expert Syst. Appl. David Martens, Christine Vanhoutte, Sophie De Winne, Bart Baesens, Luc Sels, Christophe Mues
2011	J	`jnl`	Performance of classification models from a user perspective. Decis. Support Syst. David Martens, Jan Vanthienen, Wouter Verbeke, Bart Baesens
2011	J	`jnl`	Process discovery in event logs: An application in the telecom industry. Appl. Soft Comput. Stijn Goedertier, Jochen De Weerdt, David Martens, Jan Vanthienen, Bart Baesens
2010	J	`jnl`	An overview and framework for PD backtesting and benchmarking. J. Oper. Res. Soc. G. Castermans, David Martens, Tony Van Gestel, Bart Hamers, Bart Baesens
2010	—	`ch.`	Building Acceptable Classification Models. Data Mining David Martens, Bart Baesens
2010	J	`jnl`	Credit rating prediction using Ant Colony Optimization. J. Oper. Res. Soc. David Martens, Tony Van Gestel, Manu De Backer, Raf Haesen, Jan Vanthienen, Bart Baesens
2010	J	`jnl`	From linear to non-linear kernel based classifiers for bankruptcy prediction. Neurocomputing Tony Van Gestel, Bart Baesens, David Martens
2010	—	`conf`	Software Effort Prediction Using Regression Rule Extraction from Neural Networks. ICTAI (2) Rudy Setiono, Karel Dejaeger, Wouter Verbeke, David Martens, Bart Baesens
2009	J	`jnl`	50 years of data mining and OR: upcoming trends and challenges. J. Oper. Res. Soc. Bart Baesens, Christophe Mues, David Martens, Jan Vanthienen
2009	J	`jnl`	Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE Trans. Knowl. Data Eng. David Martens, Bart Baesens, Tony Van Gestel
2009	—	`conf`	Including Domain Knowledge in Customer Churn Prediction Using AntMiner+. DMM@ICDM Wouter Verbeke, Bart Baesens, David Martens, Manu De Backer, Raf Haesen
2009	J	`jnl`	Inferring comprehensible business/ICT alignment rules. Inf. Manag. Bjorn Cumps, David Martens, Manu De Backer, Raf Haesen, Stijn Viaene, Guido Dedene, Bart Baesens, Monique Snoeck
2009	J	`jnl`	Robust Process Discovery with Artificial Negative Events. J. Mach. Learn. Res. Stijn Goedertier, David Martens, Jan Vanthienen, Bart Baesens
2008	J	`jnl`	Building acceptable classification models for financial engineering applications: thesis summary. SIGKDD Explor. David Martens
2008	J	`jnl`	Mining software repositories for comprehensible software fault prediction models. J. Syst. Softw. Olivier Vandecruys, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, Raf Haesen
2008	J	`jnl`	Predicting going concern opinion with data mining. Decis. Support Syst. David Martens, Liesbeth Bruynseels, Bart Baesens, Marleen Willekens, Jan Vanthienen
2008	—	`ch.`	Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring. Rule Extraction from Support Vector Machines David Martens, Johan Huysmans, Rudy Setiono, Jan Vanthienen, Bart Baesens
2007	J	`jnl`	Classification With Ant Colony Optimization. IEEE Trans. Evol. Comput. David Martens, Manu De Backer, Raf Haesen, Jan Vanthienen, Monique Snoeck, Bart Baesens
2007	J	`jnl`	Comprehensible credit scoring models using rule extraction from support vector machines. Eur. J. Oper. Res. David Martens, Bart Baesens, Tony Van Gestel, Jan Vanthienen
2007	—	`conf`	Process Mining as First-Order Classification Learning on Logs with Negative Events. Business Process Management Workshops Stijn Goedertier, David Martens, Bart Baesens, Raf Haesen, Jan Vanthienen
2006	—	`conf`	Ant-Based Approach to the Knowledge Fusion Problem. ANTS Workshop David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Christophe Mues, Jan Vanthienen
2006	—	`ch.`	Ants Constructing Rule-Based Classifiers. Swarm Intelligence in Data Mining David Martens, Manu De Backer, Raf Haesen, Bart Baesens, Tom Holvoet
2006	—	`conf`	Country Corruption Analysis with Self Organizing Maps and Support Vector Machines. WISI Johan Huysmans, David Martens, Bart Baesens, Jan Vanthienen, Tony Van Gestel
2005	—	`conf`	A Stigmergy Based Approach to Data Mining. Australian Conference on Artificial Intelligence Manu De Backer, Raf Haesen, David Martens, Bart Baesens